2024/01/19 15:46

This vignette is described as a sample output using hubVis package, one of the applications of Hubverse. By using it the model projections(with quantile data) will be plotted.

For more information about the Hubverse standard format, please refer to the HubDocs website.

Plots are generated by applying:

  • “interactive” format: a Plotly output object with interactive legend, hover text, zoom-in and zoom-out options, etc.

Load and Filter Data

The two data sets will be used as an example:

  • model output for a round associated with the origin date will be concatenated, target: “incident case”, for the US national level, from the example-complex-scenario-hub. The data set also does not contain an ensemble calculated by applying the function for example: hubEnsembles::simple_ensemble(df_round1, agg_fun = "median")

  • rsvnet_hospitalization.csv: example of target data which comes from the rsv-scenario-modeling-hub repo

Load data

Data Preparation

The model output data in the projection_data object follows the structure of the model_out_tbl class. This dataset is converted to a model_out_tbl object after being read-in above. In addition to the standard requirements for this class, the plot_step_ahead_model_output() function in hubVis requires that the dataset have a column whose value corresponds to the variable that should be used for the x-axis of a “step ahead” plot. In general, this should be a date variable that corresponds to the date which is the “target” of a particular prediction. By default it will look for the "target_date" column, although this could be over-ridden by specifying a different column using the x_col_name argument. In our example data, this column does not exist, so we add it below:

#> # A tibble: 6 × 10
#>   model_id origin_date target horizon location age_group target_date output_type
#>   <chr>    <date>      <chr>    <int> <chr>    <chr>     <date>      <chr>      
#> 1 team-mo… 2023-11-26  inc h…       1 US       0-0.99    2023-12-02  quantile   
#> 2 team-mo… 2023-11-26  inc h…       1 US       0-0.99    2023-12-02  quantile   
#> 3 team-mo… 2023-11-26  inc h…       1 US       0-0.99    2023-12-02  quantile   
#> 4 team-mo… 2023-11-26  inc h…       1 US       0-0.99    2023-12-02  quantile   
#> 5 team-mo… 2023-11-26  inc h…       1 US       0-0.99    2023-12-02  quantile   
#> 6 team-mo… 2023-11-26  inc h…       1 US       0-0.99    2023-12-02  quantile   
#> # ℹ 2 more variables: output_type_id <dbl>, value <dbl>
#> # A tibble: 6 × 6
#>   location date       age_group target    value population
#>   <chr>    <date>     <chr>     <chr>     <dbl>      <dbl>
#> 1 47       2016-10-08 18-130    rate hosp  0.2     5126526
#> 2 47       2016-10-08 18-130    inc hosp  10.3     5126526
#> 3 41       2016-10-08 65-130    rate hosp  0.4      681767
#> 4 41       2016-10-08 65-130    inc hosp   2.73     681767
#> 5 27       2016-10-08 18-49     rate hosp  0       2280031
#> 6 27       2016-10-08 18-49     inc hosp   0       2280031

Round Results

By Team (Simple plot)

The projection_data and truth_data contain information for multiple locations, and scenarios.

To plot the model projections for the US, No Scenario id :

By Location

By Age group (cannot run)

##{r plot_us_facet_byagegroup, fig.height=32, include=FALSE} #projection_data <- dplyr::filter(projection_data_all, location ==default_location, target == default_target, # #model_id == default_model # ) #projection_data$age_group <- as.character(projection_data$age_group) # #truth_data <- dplyr::filter(truth_data_all, location == default_location, target == default_target, # time_idx < min(projection_data$target_date) , # time_idx > "2023-08-01") #truth_data$age_group <- as.character(truth_data$age_group) #plot_step_ahead_model_output(projection_data, truth_data, # use_median_as_point = TRUE, # facet = "age_group", facet_scales = "free_x", # facet_nrow = 7,facet_ncol = 2, facet_title = "top left", show_legend = TRUE) #

Other Layouts

Multiple layout update are possible:

  • Not showing the truth data in the plot:

  • Change palette color and behavior:

    • The default palette can be changed. All the available palette names are available here:

It is possible to use only blues for all models, by setting the pal_color parameter to NULL. This might be especially useful when used for many models in conjunction with highlighting the ensemble forecast using the ens_name and ens_color argument.

The default blue color can be changed with the one_color parameter

  • Column Names:

The input data frames can have different column names for the date information. In this case, the two x_col_name and x_truth_col_name parameters can be used to indicate the variables that should be mapped to the x-axis.